Search Results for author:
Found 7 papers, 5 papers with code
Pseudo-Bag Mixup Augmentation for Multiple Instance Learning-Based Whole Slide Image Classification
Experimental results show that PseMix could often assist state-of-the-art MIL networks to refresh their classification performance on WSIs.
ProtoDiv: Prototype-guided Division of Consistent Pseudo-bags for Whole-slide Image Classification
Due to the limitations of inadequate Whole-Slide Image (WSI) samples with weak labels, pseudo-bag-based multiple instance learning (MIL) appears as a vibrant prospect in WSI classification.
Shared Coupling-bridge for Weakly Supervised Local Feature Learning
Sparse local feature extraction is usually believed to be of important significance in typical vision tasks such as simultaneous localization and mapping, image matching and 3D reconstruction.
AdvMIL: Adversarial Multiple Instance Learning for the Survival Analysis on Whole-Slide Images
To tackle these problems, we propose a novel adversarial multiple instance learning (AdvMIL) framework.
DSCA: A Dual-Stream Network with Cross-Attention on Whole-Slide Image Pyramids for Cancer Prognosis
Our experiments and ablation studies verify that (i) the proposed DSCA could outperform existing state-of-the-art methods in cancer prognosis, by an average C-Index improvement of around 4. 6%; (ii) our DSCA network is more efficient in computation -- it has more learnable parameters (6. 31M vs. 860. 18K) but less computational costs (2. 51G vs. 4. 94G), compared to a typical existing multi-resolution network.
Geometry Attention Transformer with Position-aware LSTMs for Image Captioning
In recent years, transformer structures have been widely applied in image captioning with impressive performance.
Multi-view Subspace Clustering via Partition Fusion
Multi-view clustering is an important approach to analyze multi-view data in an unsupervised way.
